In vivo bone strain and finite element modeling of a rhesus macaque mandible during mastication
Bibliographic record
Abstract
Finite element analysis (FEA) is a commonly used tool in musculoskeletal biomechanics and vertebrate paleontology. The accuracy and precision of finite element models (FEMs) are reliant on accurate data on bone geometry, muscle forces, boundary conditions and tissue material properties. Simplified modeling assumptions, due to lack of in vivo experimental data on material properties and muscle activation patterns, may introduce analytical errors in analyses where quantitative accuracy is critical for obtaining rigorous results. A subject-specific FEM of a rhesus macaque mandible was constructed, loaded and validated using in vivo data from the same animal. In developing the model, we assessed the impact on model behavior of variation in (i) material properties of the mandibular trabecular bone tissue and teeth; (ii) constraints at the temporomandibular joint and bite point; and (iii) the timing of the muscle activity used to estimate the external forces acting on the model. The best match between the FEA simulation and the in vivo experimental data resulted from modeling the trabecular tissue with an isotropic and homogeneous Young’s modulus and Poisson’s value of 10 GPa and 0.3, respectively; constraining translations along X,Y, Z axes in the chewing (left) side temporomandibular joint, the premolars and the m1; constraining the balancing (right) side temporomandibular joint in the anterior-posterior and superior-inferior axes, and using the muscle force estimated at time of maximum strain magnitude in the lower lateral gauge. The relative strain magnitudes in this model were similar to those recorded in vivo for all strain locations. More detailed analyses of mandibular strain patterns during the power stroke at different times in the chewing cycle are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".